Special propose analog processors have been previously developed for computationally intensive machine vision tasks such as edge detection and noise point removal. Such processors can be implemented on a single VLSI chip using a standard CMOS fabrication process. Generally, they are compact, low cost, low power systems that can accept raw image data and provide a digital output (such as an edge map), a processed gray-level image, or both. Many of these chips include an array of photodetectors so that an image can be focused directly onto the surface of the die. Other versions allow external data to be scanned in, as from an infrared focal-plane array, for example. The resolution is limited primarily by the fabrication process linewidth, the maximum die size, and the circuit design.
Image smoothing, which can be implemented with a resistive grid, is generally the first step in edge detection. At any given pixel, the presence of an edge can be determined by comparing various quantities at or near that pixel. The difference between two adjacent pixels of a smoothed image can be used to detect an edge. If the absolute value of this difference exceeds a certain threshold, an edge is declared. This can be done on an image processor chip with a voltage comparator. The brightness at each pixel is represented by a voltage source, and the smoothed voltage appears on the resistor network (or "grid"). The resistors connecting adjacent pixels can be denoted Hres ("horizontal" resistors), and those connecting the data source to the smoothed node can be denoted Vres ("vertical" resistors), the nature of the smoothing being determined by their i-v curves. The comparator at a pixel opens a switch in series with the Hres if and only if the voltage across the Hres (of either sign) exceeds a certain threshold, corresponding to a reasonably definite edge. The network, however, has hysteresis: when a switch opens, the comparator voltage increases even further. To assure a unique solution for each image frame, the thresholds must be raised temporarily (to close all the switches) and then gradually lowered. Thus, the strongest edges get detected first. Once the edges are found, the network provides a smoothed and segmented image in which edges remain sharp while fixed-pattern noise is smoothed out.
The Hres, comparator, and switch of a machine vision network can be thought of as a single, two-terminal element that functions as a resistive fuse. Known transistor circuits with a fuse-like response have varying degrees of adjustability. Many of such known circuits, however, do not break abruptly when the voltage exceeds a threshold, which is why standard CMOS versions tend to be bulky. Although there are some relatively compact circuits that have adjustable thresholds and break abruptly, edge detection and image segmentation networks built from these fuses all have the property that separate sets of transistors are used for the Vres and the Hres fuse blocks. A Vres circuit is typically built using a differential-input transconductance amplifier connected as a unity-gain buffer and run in subthreshold mode to give a high closed-loop output impedance.
Because of the limitations of prior art circuits, an improved comparator and transconductor circuit design is needed for (1) edge detection and image smoothing/segmentation networks, and (2) outlier (or noise point) detection and removal networks. In particular, a multiple input comparator/transconductor circuit is needed to provide effective performance in machine vision networks while conserving processor chip area per pixel.